This paper looks at the implications of climate change and the use of General Circulation Models, the risks involved and the uncertainties with predicting future climate outcomes with a focus on Australia and probable impacts on various industries and the biota of the Australian Continent. One thing we know for sure is that climate change is happening and the planet is getting warmer. Twelve of warmest years have occurred in the 1900’s, 10 having occurred between 1987 and 1998. The top 300 metres of the sea surface temperature has increased, which may have catastrophic consequences for the planet if the conveyer belt system is changed or halted. With increased terrestrial temperatures diseases are now migrating into regions that they were not found in before. Evidence suggests that with the increased heating of the planet, energy availability will increase to drive extreme weather events, which could have a ripple effect throughout the Earth system with local, regional and global positive feedbacks feeding on each other, amplifying and accelerating warming. Due to the uncertainties in climate modeling we need to develop more accurate GCMs, which we need to link to the impacts they have on decision making, as predictions do not automatically transfer to the management of natural disasters as can be seen in the resent New Orleans Cyclone impact. As forecasting using GCMs only deals in probabilities, a cohesive and collaborative research approach to climate change is required to achieve success in assessing risk and uncertainty.

Allocation of fixed proportions of land to developed, agricultural, forest, and other uses in a watershed was modeled as an optimization problem faced by a single user. Two time period cross-sectional data for 60 watersheds were used in the analysis. A multinomial logit model was used to explain the effect of population density, mean age, market concentration, travel time to work, road accessibility, personal income, education level and longitude and latitude of watersheds. Developed land use share was positively related to higher market concentrations and road accessibility, but with a higher average time to work, suggesting a rural-urban job interface. Personal income had significant and negative influence in agricultural land share, which in contrast, was increased with higher proportion of people with college degrees. As the largest city in the study area is blocked by a major river in its west, longitude had a negative influence on developed land share and a positive influence on agricultural, forest and other land use. Latitude positively influenced developed, agricultural and other land shares but negatively influenced share of forestland as the study area is on U.S. costal plains.